Are Current AML Solutions Inspired By AI-driven Approaches?
Like the world has been affected by Covid-19, the financial sector is suffering from money laundering as well. The principal objective of money launderers is to legalize black money, and financial businesses are the best place to do it. Cash-based money laundering is easy to combat; but since now criminals use digital channels for this purpose, it has become challenging. Moreover, it is hard to analyse the money trail of digital currencies offered by Virtual Asset Service Providers (VASPs).
The Advent of Digital Money Laundering
Customer behaviour in the financial sector is changing continuously. From standing in queues for payments to doing them in milliseconds using a banking app without any restriction of time. Previously, customers were bound to visit the bank at fixed timings, which is not the case now.
Identity frauds, crypto money laundering and account takeovers are the latest risks businesses are exposed to.
Internet banking allows customers to perform transactions through their online accounts using a mobile phone or a computer. Nevertheless, the system has also enabled digital money laundering through customer accounts. They also create pseudo accounts using stolen or synthetic information. Not just common people, but criminals are also beneficiaries of AI-based technologies. Now a money launderer can clean his funds through the digital banking system instead of physical offshore transfer.
Conventional techniques of financial law enforcement agencies can’t prove much effective in digital money laundering. Likewise, every operation has been modified, the AML compliance program should also use AI-driven approaches.
There has always been a debate about the risk and opportunities of integrating extended technologies in the existing system. Financial institutions are at the dawn of their transformation journey but are not aware of the pros and cons. So what are the principle concepts of using AI in the AML systems?
Constitute Solid Administration
Establishing a strong administration and controls over the design and algorithms of AI will be very critical in the compliance of the AML program. Good administration gives the substances of risk assessment & management, improves the effectiveness of AML screening and transaction monitoring likewise. AI drives the significant levels of identification and verification and helps in decision-making in the entire process of AML (Anti-Money Laundering).
The initial stage for the development of AI administration and checks may be to furnish and adapt previously available risk-based approaches. The AML methodologies have been progressively modified and improved in the past decade. The financial sector can utilize the same regulatory framework as the basis from where to start a consistent AML risk-based approach. This will address all the requirements of regulatory authorities.
Defining Scope and Success Criteria
The foundation of AI-powered solutions to curb money laundering should start with a transparent checklist of objectives. They can ensure that the implementation is aligned with the design and scope of the AML solutions. The improved investigative and intelligence abilities of AI can yield substantial benefits surpassing obligatory controls.
Making red flags linked with the risk statements. These KPIs will show the extent to which the output is meeting the objectives. While defining the scope of AML solutions, the data protection laws should be in adherence. The policy should be clear on the use and authorization of personal data. The privacy protocols are meant to be followed for legal compliance.
Transparent Designing of AML Steps
The strength to explain and audit compliance is the keystone of the modern AML landscape. Considering this, the AI algorithms should be transparent. AI and ML are vast fields with changing levels of complexity. Deep learning and neural networks can enhance complex areas while building trust.
The design process must consider the different capabilities and improvement areas through which it can produce more reliable results. The characteristics and usage of input data should reflect the objectives. The design process must certify the technical requirements. They should satisfy the limitations and design for administration review.
Shufti Pro’s Take on AML Screening
Shufti Pro’s AML solution can screen individuals against criminal lists, filtering the high-risk from the low-risk customers. CDD (Customer Due Diligence) or EDD (Enhanced Due Diligence) is performed on the customers according to their risk profile. Ongoing monitoring and background screening mitigate the hazards of potential money laundering by a low-risk customer. The artificially intelligent software is aligned with all the objectives of a competent AML program.
